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Setting up Kubuntu 13.04 for research

A few days ago, Kubuntu 13.04 Raring Ringtail was released. I am a Kubuntu user myself, and to celebrate this new release, I wanted to share a few tips on how to set up the perfect Kubuntu environment for neuroscientists and psychologists. Of course, the ‘perfect’ environment is different everyone, but there are a few things that almost every researcher in this field will need: An office suite, a reference manager, graphics software, statistics and analysis software, and experiment building software.

What is Kubuntu?

Kubuntu is a Linux distribution. If you’re not familiar with Linux, this may not mean much to you, so let’s start with a little background.


Some relations between various flavors of Linux.

A Linux-based operating system is a layer cake. It consists of many layers of software that can be stacked and combined in an infinite number of ways. Only the bottom layer is constant: That’s the Linux kernel, which is part of all Linux-based operating systems, including Android. On top of the kernel, there can be different layers of software. Kubuntu is essentially one specific selection of software. Other Linux distributions, such as openSUSE, have slightly different selections. Some differences are clearly visible, such as different desktop environments (i.e. the software that controls the start menu, etc.). Other differences are largely under the hood, such as different system management tools.

A Linux distribution arranges the many layers of software in such a way that you, as a user, don’t have to worry about how it works.

The preference for one Linux distribution over another is entirely one of taste. I like Kubuntu because it is built on Ubuntu and Debian. These are major distributions that offer a lot of software out of the box, and you can rely on them to provide regular updates and support. The difference between Ubuntu and Kubuntu is the desktop interface. Ubuntu uses Unity, which is good-looking, but highly simplified. Kubuntu uses KDE, which offers more flexibility. I particularly like Kate, the default KDE text editor, which is truly excellent for programming. The entire Ubuntu family shares the same innards, so the tips from this post apply to all of them.

 
A bit about patches, textures, and masks in PsychoPy

PsychoPy is a powerful Python library for creating the type of stimuli that are frequently used in psychological and neuroscientific experiments. I use it all the time, mostly from within OpenSesame, but I remember that I initially found working with PsychoPy quite daunting. This is because PsychoPy takes a very different approach to stimulus generation than most people are used to. You have to think in terms of patches, textures, and, masks, rather than in conventional drawing primitives, such as rectangles and lines (although newer versions of PsychoPy also support these drawing primitives). Therefore, I decided to write a short tutorial that explains the basics of working with PsychoPy.

 
An easy way to create graphs with within-subject error bars

Let's consider an experiment in which participants were shown happy pictures (warning: this is a silly experiment, without a proper control condition). Before and after they saw the pictures, they filled in a questionnaire to estimate their mood on a scale from 1 (sad) to 10 (happy). The results of the experiment are shown in the graph below. Each line represents a single participant.


Clearly, people became happier after seeing the happy pictures. This can also be verified easily using a paired samples t-test, which shows that the “before” scores are significantly lower than the “after” scores (p < .005).

 
OpenSesame demonstration video

I finally got around to creating an OpenSesame demonstration video. It's essentially an annotated screencast of me creating an "affordance/ orientation-effect" type of experiment in slightly over five minutes. The video is not intended to be a comprehensive tutorial, but it might give you a nice feel of what working with OpenSesame is like. Here OpenSesame is running on Ubuntu 10.10, but things work the same on Windows and Mac OS. You can download the resulting experiment here (contains images from Rossion & Pourtois, 2004).

For those who are interested: The experiment is based on a paper by Symes et al. (2005) and the images are taken from the excellent and freely available set of images by Rossion & Pourtois (2004).

References

Rossion, B., & Pourtois, G. (2004). Revisiting Snodgrass and Vanderwart’s object pictorial set: The role of surface detail in basic-level object recognition. Perception, 33(2), 217-236.

Symes, E., Ellis, R., & Tucker, M. (2005). Dissociating object-based and space-based affordances. Visual cognition, 12(7), 1337-1361.

 
Using non-western alphabets in OpenSesame (e.g., Chinese, Hebrew or Arabic)

This post has been moved to the OpenSesame documentation area: http://osdoc.cogsci.nl/miscellaneous/non-western-alphabets